Paper: Edit Detection And Parsing For Transcribed Speech

ACL ID N01-1016
Title Edit Detection And Parsing For Transcribed Speech
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2001

We present a simple architecture for parsing transcribed speech in which an edited-word de- tector rst removes such words from the sen- tence string, and then a standard statistical parser trained on transcribed speech parses the remaining words. The edit detector achieves a misclassi cation rate on edited words of 2.2%. (The NULL-model, which marks everything as not edited, has an error rate of 5.9%). To evalu- ate our parsing results we introduce a new eval- uation metric, the purpose of which is to make evaluation of a parse tree relatively indi erent to the exact tree position of EDITED nodes. By this metric the parser achieves 85.3% precision and 86.5% recall.